Skin Lesion Classification Using Convolutional Neural Network and ABCRule

Abstract

Skin cancer, which can occur in any part of the human skin, is one of the common and serious types of cancer. Accurate diagnosis and segmentation of lesions are crucial to the early diagnosis. Computer-aided diagnosis make important contributions to help doctors in the diagnosis of cancer from skin images. The most important factor for such systems to reveal the accurate results is the correct feature extraction. In this study, a model for the classification of seven types of skin lesions is developed by combining the features of CNN-based feature extraction and the ABCD rule, which is widely used in the clinic. The model is evaluated on HAM10000 well-known dataset. The classification results obtained with different combinations of features and machine learning algorithms are compared. According to the results, the best classification accuracy is obtained with the Cosine Similarity Classifier with 96.4% when the features determined by CNN and the features in the ABCD rule are used together. © 2024 Elsevier B.V., All rights reserved.